METHOD FOR OBSERVING A SAMPLE

Abstract

A method for observing a sample is provided, including illuminating the sample with a light source and forming a plurality of images, by an imager, the images representing the light transmitted by the sample in different spectral bands. From each image, a complex amplitude representative of the light wave transmitted by the sample is determined in a determined spectral band. The method further includes backpropagation of each complex amplitude in a plane passing through the sample, determining a weighting function from the back-propagated complex amplitudes, propagating the weighting function in a plane along which the matrix photodetector extends, updating each complex amplitude, in the plane of the sample, according to the weighting function propagated.

Description

TECHNICAL FIELD

The technical field of the invention is related to the observation of a sample, in particular a biological sample, by lensless imaging, implementing a holographic reconstruction algorithm with improved performance.

PRIOR ART

The observation of samples, and in particular biological samples, by lensless imaging has seen substantial growth over the last 10 years. This technique allows a sample placed between a light source and a matrix-array photodetector to be observed without the need to place any magnifying optical lenses between the sample and the photodetector. Thus, the photodetector collects an image of the light wave transmitted by the sample.

This image is formed of interference patterns formed by interference between the light wave emitted by the source and transmitted by the sample, and diffracted waves resulting from the diffraction, by the sample, of the light wave emitted by the source. These interference patterns are sometimes referred to as diffraction patterns.

Document WO2008090330 describes a device allowing biological samples, in fact cells, to be observed by lensless imaging. The device allows an interference pattern to be associated with each cell, the morphology of the interference pattern allowing the type of cell to be identified. Lensless imaging would thus appear to be a simple and inexpensive alternative to conventional microscopy. In addition, its field of observation is clearly larger that of a microscope can be. Thus it will be understood that the potential avenues of application associated with this technology are many. This document also mentions that it is possible to illuminate the sample using light sources of various wavelengths.

Document US2012/0218379, published after the preceding document, echoes most of the teachings of WO2008090330, while also mentioning the possible use of a color matrix-array photodetector; however, the color information is subsequently processed to form a monochromatic image.

Generally, the image formed on the matrix-array photodetector, including the interference patterns, may be processed with a digital propagation algorithm, so as to estimate optical properties of the sample. Such algorithms are well known in the field of holographic reconstruction. To do this, the distance between the sample and the photodetector being known, a propagation algorithm taking into account this distance, and the wavelength, is applied. It is then possible to reconstruct an image of an optical property of the sample. A digital reconstruction algorithm is for example described in US2012/0218379.

It is also known that such algorithms may generate an image affected by a source of substantial background noise, referred to as the “twin image”. Such noise is due to the fact that the initial image, formed by the photodetector, contains only partial information on the light wave collected by the photodetector. Specifically, an image provides information only on the real part of the light wave, this information being obtained from the measured intensity. However such an image contains no information on the imaginary part of the light wave to which the detector is exposed, in particular its phase. The reconstruction algorithm therefore uses incomplete information, this resulting in the appearance of noise in the reconstructed image.

Such background noise may complicate the interpretation of images reconstructed by digital propagation; it is important to decrease the influence thereof via suitable algorithms.

To do this, the publication “Lensless phase contrast microscopy based on multiwavelength Fresnel diffraction”, Optics Letters Vol. 39, No. 2, 15 Jan. 2014, describes an algorithm allowing the quality of reconstructed images to be improved. This publication describes a lensless imaging device based on the use of three light sources of different wavelengths (685 nm, 785 nm and 940 nm, respectively). The sample is illuminated in succession by these three light sources. The photodetector then acquires as many images as there are light sources, these images being formed in a plane, called the detector plane, in which the sensor lies. To each image corresponds one wavelength.

A first image, of a first wavelength, is back propagated, depending on said first wavelength, to a plane in which the object lies, called the object plane, so as to obtain, in this object plane, a complex first field. The phase of this complex first field, in the object plane, is multiplied by a ratio between the first wavelength and a second wavelength. This complex field is then propagated, depending on said second wavelength, from the object plane to the detector plane, subsequent to which its modulus is replaced by the modulus of the image acquired at said second wavelength. It is then back propagated to the object plane, for a second iteration. The iterative method continues until a convergence criterion has been reached. Document WO2014035238, certain of the inventors of which are the authors of the aforementioned publication, contains the same teachings.

The publication Bao P, “Optical surface profile measurement using phase retrieval by tuning the illumination wavelength”, Optics Communications 285, 5029-5036, 2012 describes an iterative algorithm allowing a three-dimensional surface of a transparent object to be reconstructed on the basis of an image acquired by an image sensor in a lensless configuration. This algorithm aims to illuminate an object using two wavelengths. The image acquired by the image sensor, at each wavelength, is propagated to an object plane. A phase difference is then estimated, at each of these wavelengths, in the object plane. The three-dimensional surface is reconstructed on the basis of the phase difference thus estimated.

Document WO2015/015023 describes an iterative holographic reconstruction algorithm based on a color image sensor in a lensless configuration. According to this algorithm, the sample is illuminated with a polychromatic light source. The acquired image is decomposed into various spectral components. Each component is back propagated to a plane in which the sample lies, the propagation of the components being carried out over distances that are different from one another. This document then describes mixing spectra of each hologram in the Fourier domain, this allowing a single image to be generated, the latter being used in a subsequent iterative loop.

The inventors provide an alternative method to those provided in the preceding publications, allowing optical properties of a sample to be reliably estimated.

SUMMARY OF THE INVENTION

A first subject of the invention is a method for observing a sample, including the following steps:

• • i) illuminating said sample using a light source that is able to produce a light wave that propagates along a propagation axis;
  • ii) acquiring, using a photodetector, a plurality of images of the sample, said images being formed in a detection plane, the sample being placed between the light source and the photodetector, each image being representative of a light wave, transmitted by the sample, under the effect of said illumination, each image being acquired in a spectral band that is different from the others;
    the method being characterized in that it also comprises the following steps:
  • iii) determining, on the basis of each image acquired in a spectral band, an initial complex amplitude of the transmitted light wave, in said spectral band, in said detection plane;
  • iv) back propagating each complex amplitude established in the detection plane, in a spectral band, in order to determine a complex amplitude of the transmitted wave, in each spectral band, in a plane in which the sample lies;
  • v) combining a plurality of complex amplitudes determined in step iv), in various spectral bands, in order to calculate a weighting function in the sample plane;
  • vi) projecting said weighting function onto the detection plane so as to obtain, for each spectral band, a weighting function in said detection plane;
  • vii) updating each complex amplitude of the transmitted light wave, in each spectral band, in the detection plane, using said weighting function determined, in said spectral band, in step vi);
  • viii) repeating steps iv to vii until a stop criterion is reached.

Thus, each iteration comprises propagating, in an operation referred to as back propagation, from the detector plane to the sample plane, a plurality of complex amplitudes, in various spectral bands. These various complex amplitudes are combined, in the sample plane, to form a weighting function. This combination, in the sample plane, of a plurality of complex amplitudes, corresponding to various spectral ranges, has the effect of smoothing the noise affecting each thereof, this noise being the consequence of the back propagation.

The weighting function is then propagated from the sample plane to the detection plane, where it is used to form a new estimation of the complex amplitude of the light wave to which the sample is exposed, in each spectral band in question.

Alternatively to the methods described in the prior art, the weighting function, in said sample plane, may be calculated by calculating a weighted sum of various complex amplitudes, or of their logarithms, in the sample plane, of the transmitted light wave, said complex amplitudes respectively being associated with various spectral bands. The weighting function, in said sample plane, may also be determined by calculating a weighted sum of the modulus and/or argument of various complex amplitudes, in the sample plane, of the transmitted light wave, said complex amplitudes respectively being associated with various spectral bands.

By transmitted light wave, what is meant is the light wave to which the photodetector is exposed.

The method may comprise any one of the following features alone or in any technically possible combination:

• • In step iii), the modulus of the complex amplitude of the transmitted wave transmitted in a spectral band is determined by normalizing the intensity of the image measured by the photodetector 16, in said spectral band, by a reference intensity measured by said photodetector in the absence of sample.
  • In step iv), the complex amplitude in a sample plane, in a spectral band, is determined by applying a propagation-axis propagation operator to said complex amplitude defined in the same spectral band in the detection plane.
  • In step vi), said weighting function, in the detection plane, is obtained by applying a propagation-axis propagation operator to the weighting function determined, in the sample plane, in step v).
  • In step vii), the modulus of the complex amplitude of the transmitted light wave, in a spectral band, in the detection plane, is calculated depending on the modulus of said initial complex amplitude, in said spectral band.
  • In step vii), the argument of the complex amplitude of the transmitted light wave, in a spectral band, in the detection plane, is calculated depending on the argument of the weighting function determined, in said detection plane and in said spectral band, in step vi).

In step v), said weighting function may be common to all the spectral bands. Alternatively, this step v) may comprise determining a plurality of weighting functions, each weighting function being associated with one spectral band.

The method may include, following step viii), the step ix) of forming an image representative of the modulus or of the argument of the complex amplitude of the wave transmitted by the sample, in the sample plane or in the detection plane, in at least one spectral band (λ_i).

Another subject of the invention is a device for observing a sample including:

• • a light source that is suitable for illuminating said sample;
  • a photodetector, the sample being placed between the light source and the photodetector,
  • the photodetector being suitable for forming a plurality of images, in a detection plane, of a light wave transmitted by the sample under the effect of illumination by said light source, each image being obtained in a spectral band that is different from the others; and
  • a processor that is suitable for processing said plurality of images by executing instructions, programmed into a memory, implementing a method such as described above.

FIGURES

FIG. 1 shows a first example of a device for implementing the invention, the analyzed sample being an anatomopathology slide.

FIG. 2 shows a first example of a device for implementing the invention, the analyzed sample being a bodily liquid containing particles.

FIG. 3 shows the detection plane, on which an image is formed, and the sample plane. This figure also illustrates the relationships between the main quantities implemented in the various described embodiments.

FIG. 4 shows a flowchart illustrating the sequence of the main steps of an iterative reconstructing method.

FIG. 5 shows a second example of a device for implementing the invention, the analyzed sample being an anatomopathology slide.

FIGS. 6A, 6B and 6C show reconstructed images, in a sample plane, in a first spectral band, reconstructed using an iterative reconstruction algorithm, these images respectively being obtained following a number of iterations respectively equal to 1, 3 and 10.

FIGS. 7A, 7B and 7C show reconstructed images, in a sample plane, in a second spectral band, reconstructed using an iterative reconstruction algorithm, these images respectively being obtained following a number of iterations respectively equal to 1, 3 and 10.

FIGS. 8A, 8B and 8C show reconstructed images, in a sample plane, in a third spectral band, reconstructed using an iterative reconstruction algorithm, these images respectively being obtained following a number of iterations respectively equal to 1, 3 and 10.

FIGS. 9A, 9B and 9C show composite images obtained by combining reconstructed images in the first, second and third spectral bands, these images respectively corresponding to 1, 3 and 10 iterations. These images are color images, here shown in black and white.

SUMMARY OF PARTICULAR EMBODIMENTS

FIG. 1 shows an example of a device falling within the scope of the invention. A light source 11 is able to emit a light wave 12, called the incident light wave, in the direction of a sample 10, along a propagation axis Z.

The sample 10 may be a biological sample that it is desired to characterize. It may for example be a tissue slide, or an anatomopathology slide, including a small thickness of tissue deposited on a transparent slide 15. By small thickness, what is meant is a thickness preferably smaller than 100 μm, and preferably smaller than 10 μm, typically a few microns. Such a sample is shown in FIG. 1. It may be seen that the sample lies in a plane P₀, called the sample plane, perpendicular to the propagation axis Z.

The sample 10 may also include a solid or liquid medium 14 containing particles 1, 2, 3, 4, 5 to be characterized, such a case being shown in FIG. 2. It may for example be a question of biological particles in a culture medium, or in a bodily liquid. By biological particle, what is meant is a cell, a bacterium or another microorganism, a mushroom, a spore, etc. The term particles may also designate microbeads, for example metal microbeads, glass microbeads or organic microbeads, which are commonly implemented in biological protocols. It may also be a question of insoluble droplets submerged in a liquid medium, for example lipid droplets in an oil-in-water type emulsion. Thus, the term particle designates both endogenous particles, initially present in the examined sample, and exogenous particles, added to this sample before analysis.

Generally, a particle has a size advantageously smaller than 1 mm, or even smaller than 500 μm, and preferably a size comprised between 0.5 μm and 500 μm.

The distance Δ between the light source and the sample is preferably larger than 1 cm. It is preferably comprised between 2 and 30 cm. Preferably, the light source, seen by the sample, may be considered to be a point source. This means that its diameter (or its diagonal) is preferably smaller than one tenth and better still one hundredth of the distance between the sample and the light source. Thus, preferably, the light reaches the sample in the form of plane waves, or waves that may be considered as such.

The light source 11 is able to produce a plurality of incident light waves 12₁ . . . 12_n, each i^th light wave 12_i lying in an i^th spectral band λ_i. The spectral bands 12₁ . . . 12_n are different from one another, and, preferably, do not overlap.

In the example device shown in FIGS. 1 and 2, the light source includes three elementary light sources, namely three light-emitting diodes (LEDs) 11₁, 11₂ and 11₃ emitting in the spectral bands λ₁=450 nm-465 nm; λ₂=520 nm-535 nm; A₃=620 nm-630 nm, respectively. Preferably, there is no overlap between the various spectral bands; a negligible overlap, for example concerning less than 25% and better still less than 10% of the light intensity emitted, is however envisionable. In this example, the light source 11 includes a Cree (registered trademark) XLamp (registered trademark) MC-E multi-LED diode. This diode includes four individually addressable elementary light-emitting diodes, only three of which are implemented in the context of this invention, the fourth being a white LED. The elementary light sources may be temporally coherent sources, such as laser diodes. Other configurations of light sources 11 are possible and described below.

The light source 11 is preferably a point source. It may in particular comprise a diaphragm 18, or spatial filter. The aperture of the diaphragm is typically comprised between 5 μm and 1 mm, preferably between 50 μm and 500 μm, and is for example 150 μm. The diaphragm may be replaced by an optical fiber, a first end of which is placed facing one elementary light source 11₁, 11₂ or 11₃, and a second end of which is placed facing the sample.

The light source 11 preferably includes a diffuser 17, placed between each elementary light source 11₁, 11₂ and 11₃ and the diaphragm 18. The inventors have observed that the use of such a diffuser allows constraints on the centrality of each elementary light source with respect to the aperture of the diaphragm to be relaxed. In other words, the use of such a diffuser allows an elementary light source 11i, with 1≤i≤3, that is slightly off center with respect to the aperture of the diaphragm 18 to be used. In this example, the diaphragm is sold by Thorlabs under the reference P150S.

Preferably, each elementary light source 11_i is of small spectral width, for example smaller than 100 nm, or even than 20 nm. The term spectral width designates the full width at half maximum of the emission band of the light source in question.

In this example, the diffuser implemented is a 40° diffuser (reference Light Shaping Diffuser 40°, manufactured by Luminit). The function of such a diffuser is to distribute the light beam, produced by an elementary light source 11_i, over a cone of angle α, α being equal to 40° in the present case. Preferably, the scattering angle α varies between 10° and 60°.

The sample 10 is placed between the light source 11 and a matrix-array photodetector 16. The latter preferably lies parallel, or substantially parallel to the transparent slide 15 holding the sample.

The term substantially parallel means that the two elements may not be rigorously parallel, an angular tolerance of a few degrees, smaller than 10° being acceptable.

The photodetector 16 is an imager, able to form an image in a detection plane P. In the example shown, it is a CCD or CMOS matrix-array photodetector including a pixel matrix-array. CMOS photodetectors are preferred, because the size of the pixels is smaller, thereby allowing images to be acquired the spatial resolution of which is more favorable. In this example, the detector is a CMOS sensor sold by Omnivision under the reference OV5647. It is an RGB CMOS sensor comprising 2592×1944 pixels, with an inter-pixel pitch of 1.4 μm. The useful area of the photodetector is 3.6×2.7 mm². The detection plane P preferably lies perpendicular to the propagation axis Z of the incident light wave 12.

Preferably, the photodetector comprises a pixel matrix-array, above which matrix array is placed a transparent protective window. The distance between the pixel matrix-array and the protective window is generally comprised between a few tens of μm to 150 or 200 μm. Photodetectors, the inter-pixel pitch of which is smaller than 3 μm, are preferred, in order to improve the spatial resolution of the image. The photodetector may comprise a mirror-type system for redirecting images toward a pixel matrix-array, in which case the detection plane corresponds to the plane in which the image-redirecting system lies. Generally, the detection plane P corresponds to the plane in which an image is formed.

The distance d between the sample 10 and the pixel matrix-array of the photodetector 16 is, in this example, equal to 300 μm. Generally, whatever the embodiment, the distance d between the sample and the pixels of the photodetector is preferentially comprised between 50 μm and 2 cm, and preferably comprised between 100 μm and 2 mm.

The absence of magnifying optics between the photodetector 16 and the sample 10 will be noted. This does not prevent focusing micro-lenses optionally being present level with each pixel of the photodetector 16, these lenses not having the function of magnifying the image.

FIG. 3 shows a sample 10, including diffracting objects 32 placed around non-diffracting or not very diffracting zones 31, which are qualified poor zones below. The sample may be solid, for example in the case of a tissue deposited on an anatomopathology slide. It may also be liquid, for example in the case of a bodily liquid or a cell culture medium.

The photodetector 16 is able to produce an image I_i of a light wave 22_i transmitted by the sample 10 when the latter is illuminated by an incident wave 12_i, in the i^th spectral band λ_i. The spectral band of the transmitted light wave 22_i includes all or some of the spectral band of the incident wave 12_i. The light wave 22_i, transmitted by the sample, in the spectral band λ_i, results from the interaction of the sample 10 with the incident light wave 12_i produced by the elementary light source 11_i.

Under the effect of the incident light wave 12_i, the sample 10 may generate a diffracted wave that is liable to produce, level with the detection plane P, interference, in particular with a portion of the incident light wave 12_i transmitted by the sample. This interference gives rise, in the image acquired by the photodetector, to a plurality of elementary diffraction patterns, each elementary diffraction pattern 36 including a central zone and a plurality of concentric diffraction rings. Each elementary diffraction pattern 36 is due to one diffracting object 32 in the sample.

Moreover, the sample may absorb a portion of the incident light wave 12_i. Thus, the light wave 22_i, in a spectral band λ_i, transmitted by the sample, and to which the matrix-array photodetector 16 is exposed, may comprise:

• • a component resulting from the diffraction, described above, this diffraction component possibly in particular resulting in the presence of elementary diffraction patterns on the photodetector 16, each elementary diffraction pattern possibly being associated with one diffracting element 32 of the sample. Such a diffracting element may be a cell, or a particle, or any other diffracting object 32 present in the sample 10.
  • a component resulting from the absorption of the incident light wave 12_i in the sample.

A processor 20, for example a microprocessor, is able to process each image generated by the matrix-array photodetector 16. In particular, the processor is a microprocessor connected to a programmable memory 23 in which a sequence of instructions for carrying out the calculating and image-processing operations described in this description is stored. It may also be connected to a display screen 24.

The steps of an iterative method for obtaining an image of the sample 10 will be described below with reference to FIGS. 3 and 4.

1^st Step: Initialization

In a first step 100 of acquiring images, each elementary light source 11_i of the light source 11 is activated in succession, each light source emitting an incident light wave (12₁, . . . 12_N), in a spectral band (λ₁, . . . λ_N), along a propagation axis Z, in the direction of the sample 10.

In each acquisition, the matrix-array photodetector captures an image I_i corresponding to a spectral band λ_i, the index i, relating to the spectral band, being comprised between 1 and N, N being the number of spectral bands in question. In the example shown in FIGS. 1 and 2, the light source 11 includes three elementary light sources 11₁, 11₂ and 11₃. The photodetector captures three images I₁, I₂, I₃, corresponding to the spectral bands λ₁, λ₂ and λ₃, respectively.

The sample is placed at an axial coordinate z=0, along the propagation axis Z. The letter r designates a radial coordinate, i.e. a coordinate in a plane perpendicular to the propagation axis Z. The plane z=d corresponds to the detection plane, whereas the plane z=0 corresponds to a plane passing through the sample, called the sample plane and denoted P₀.

If I_i^z=d(r)=I_i^d(r) designates the value of the intensity captured, in the spectral band λ_i, by the pixel of the detector of radial coordinate r in the detection plane, it is possible to establish, using the image I_i, a complex amplitude 60_i^z=d(r)=α_i^d(r) of the wave 22_i at said pixel of coordinate r, the modulus of which may be expressed by the expression:

|α_i^d(r)|=√{square root over (I_i^d(r))}

The exponent d expresses the fact that the complex amplitude is determined in the sample plane P, of equation z=d. The complex amplitude α_i^d(r) includes a modulus and an argument, such that:

α_i^d(r)=M_i^d(r)e^jφid(r)

where:

• • M_i^d(r) is the modulus of the complex amplitude of the light wave detected by the photodetector, in the i^th spectral band λ_i, at a radial coordinate r in the detection plane; and
  • φ_i^d(r) is the phase of the complex amplitude of the light wave detected by the photodetector, in the i^th spectral band λ_i, and at said radial coordinate r in the detection plane.

However, the matrix-array photodetector delivers no information on the phase of the light wave. Thus, in step 100, e^jφid(r) is considered to be equal to an arbitrary initial value, for example equal to 1.

The complex amplitude α_i^d(r) may be expressed, normalized, by the expression:

$A_i^d\left(r\right)=\frac{{\alpha }_i^d\left(r\right)}{\sqrt{I_i^{\mathrm{mean}}}}$

where:

• • I_i^mean is the mean intensity of the light wave 12_i emitted by the light source 11 in the i^th spectral band λ_i; this mean intensity may be determined experimentally, by placing the photodetector 16 facing the light source 11, without a sample placed therebetween, and by calculating the mean of the pixels of the image acquired by the photodetector 16.
  • A_i^d(r) is the normalized complex amplitude of the light wave 22_i detected by the matrix-array photodetector 16 in the i^th spectral band λ_i.

The normalization may also be carried out by dividing the complex amplitude α_i^d(r) by I_i^mean(r), this term representing the light intensity, at the radial coordinate r, measured in the absence of sample.

The normalized complex amplitude A_i^d(r) includes a modulus and an argument, such that:

A_i^d(r)=m_i^d(r)e^jφid(r)

where:

• • m_i^d(r) is the modulus of the normalized complex amplitude A_i^d(r); and
  • φ_i^d(r) is the phase of the normalized complex amplitude, which is also the phase of the complex amplitude α_i^d(r).

The first step 100 allows, on the basis of the image I_i detected by the photodetector in the i^th spectral band λ_i, an initial value to be assigned to each complex amplitude α_i^d(r) or to each normalized complex amplitude A_i^d(r), such that:

α_i,p=1^d(r)=M_i^d(r)=√{square root over (I_i^d(r))}

or

$A_{i,p=1}^d\left(r\right)=m_i^d\left(r\right)=\sqrt{\frac{I_i^d\left(r\right)}{I_i^{\mathrm{mean}}}}.$

The index p corresponds to the rank of the iteration of the iterative method described below. Step 100 being an initialization step, the value 1 is attributed to this index.

By addressing all or some of the pixels r of the photodetector 16, a complex image, or complex field, of the light wave 22_i in the detector plane is obtained, this image containing the complex amplitudes α_i^d(r) or the normalized complex amplitudes A_i^d(r).

In the rest of the description, only the normalized complex amplitude A_i^d(r) will be considered, though the reasoning also applies to the complex amplitude α_i^d(r).

This first step is repeated for each spectral band (λ₁ . . . λ_N) detected by the photodetector.

2^nd Step: Back Propagation to the Sample Plane P₀

During a second step 200, the normalized complex amplitude A_i,p^d(r) of the wave 22_i to which the detector is exposed is estimated, in the sample plane P₀. This estimation is made by back propagating the normalized complex amplitude A_i,p^d(r), determined in the detection plane P, from the detection plane P to the sample plane P₀.

The index p designates the rank of the iteration. In the first iteration (p=1), the initial normalized complex amplitude A_i,p=1^d(r)=A_i^d(r) obtained at the end of the first step 100 is used. In the following iterations (p>1), the complex amplitude resulting from the preceding iteration is used, as will be detailed below.

According to well-known principles of digital holographic reconstruction, by determining the product of a convolution between the complex amplitude of the light wave 22_i determined, for the spectral band λ_i, in the detection plane z=d, and a propagation operator h(r,z), it is possible to reconstruct a complex amplitude of the same light wave at any point of spatial coordinates (r,z), and in particular in the sample plane P₀.

In other words, the normalized complex amplitude A_i,p^z(r) of the light wave 22_i may be obtained, at a point of coordinates (r, z), on the basis of A_i,p^z=d(r), via the operation:

A_i,p^z(r)=A_i,p^z=d(r)*h_λi(r,z−d),

where h_λi is the propagation operator in the spectral band λ_i.

When the reconstruction is carried out in the direction of propagation of the light, for example from the sample to the photodetector, propagation is spoken of. When the reconstruction is carried out in the direction opposite the direction of propagation of the light, for example from the photodetector to the sample, back propagation is spoken of.

The propagation operator may in particular be based on the Fresnel diffraction model. In this example, the propagation operator is the Fresnel-Helmholtz function:

$h\left(r,z\right)=\frac{1}{j\lambda z}e^{j2\pi \frac{z}{\lambda }}\exp \left(j\pi \frac{r^2}{\lambda z}\right)$

where λ is the wavelength.

Thus,

$A_{i,p}^{z=0}\left(r\right)=A_{i,p}^0\left(r\right)=A_{i,p}^{z=d}\left(r\right)*h_{\lambda i}\left(r,-d\right)=-\frac{1}{j{\lambda }_iz}e^{-j2\pi \frac{z}{{\lambda }_i}}\int \int A_{i,p}^d\left(r^{\prime }\right)\exp -\left(j\pi \frac{{\left(r-r^{\prime }\right)}^2}{{\lambda }_id}\right){\mathrm{dr}}^{\prime }$

where

• • r′ designates the radial coordinates in the plane of the photodetector (z=d);
  • r designates the radial coordinates in the reconstruction plane (z=0); and
  • λ_i is the central wavelength of the spectral band in question.

A_i,p⁰(r) is therefore obtained by back propagating A_i,p^d(r) over the distance d separating the detection plane P from the sample plane P₀.

This second step is repeated for each spectral band (λ₁ . . . λ_N) emitted by the light source 11 or, more generally, for each spectral band (λ₁ . . . λ_N) respectively associated with each image (I₁ . . . I_N) detected by the photodetector 16.

It is possible, at this stage, to establish an image of the modulus or of the phase of the complex amplitude A_i,p⁰(r) of each light wave 22_i, in the sample plane P₀, whether the complex amplitude be normalized or not, by calculating the value of A_i,p⁰(r) at the various coordinates r in the sample plane.

Each image of the modulus of the complex amplitude A_i,p⁰(r) is representative of the intensity of the light wave level with the sample, whereas each image of the argument of the complex amplitude A_i,p⁰(r) is representative of the phase of the intensity of the light wave level with the sample.

When, as in the present case, three spectral bands centered respectively on wavelengths in the blue, green and red, are used, the information contained in the three images allows a color image of the sample to be obtained.

It will be noted that the normalized complex amplitude A_i,p⁰(r) is equivalent to a transmission function describing transmission of the incident wave 12_i by the sample 10 at the radial coordinate r.

3^rd Step: Determining the Weighting Function

In the step 300, a weighting function, denoted F_p⁰(r), allowing the complex amplitude of the light wave transmitted by the sample in the various spectral bands λ_i in question to be weighted, is determined, in the sample plane.

According to this example, the weighting function F_p⁰(r), in the sample plane, may be common to each spectral band. It is obtained by combining the normalized complex amplitudes A_i,p⁰(r) of the light wave transmitted by the sample, in the sample plane P₀ and in the various spectral bands λ_i.

According to one example, the weighting function is obtained via a weighted sum of each complex amplitude determined in step 200, in the sample plane P₀, using the expression:

$F_p^0\left(r\right)=\frac{1}{\sum _ik_i}\sum _ik_iA_{i,p}^0\left(r\right)$

where k_i is a positive weighting factor associated with the i^th spectral band λ_i.

The weighting factors may be equal to one another, for example equal to ⅓.

Other ways of determining the weighting function, in the sample plane, are detailed below.

4^th Step: Propagation of the Weighting Function to the Detector Plane

The step 400 aims to propagate, from the sample plane P₀ to the detector plane P, the weighting function F_p⁰(r) determined, in the preceding step, in the sample plane P₀. Since the propagation operator is dependent on wavelength, this propagation is carried out for each spectral band λ_i in question.

Thus, for each spectral band λ_i, F_i,p^d(r)=F_p⁰(r)*h_λi(r,z=d).

When the propagation operator is a Fresnel-Helmholtz operator such as defined above,

$F_{i,p}^d\left(r\right)=\frac{1}{j{\lambda }_id}e^{-j2\pi \frac{d}{{\lambda }_i}}\int \int F_{i,p}^0\left(r^{\prime }\right)\exp \left(j\pi \frac{{\left(r-r^{\prime }\right)}^2}{{\lambda }_id}\right){\mathrm{dr}}^{\prime }$

Since the propagation operator is dependent on wavelength, as many weighting functions are determined, in the detection plane, as there are spectral bands.

• • r′ designates the radial coordinates in the sample plane (z=0);
  • r designates the radial coordinates in the reconstruction plane, i.e. in the detector plane (z=d); and
  • λ_i is the central wavelength of the spectral band in question.

5^th Step: Update of the Complex Amplitude in the Detector Plane

In the step 500, the value of the weighting function, in the detection plane z=d, is used to update the estimation of the normalized complex amplitude A_i,p^d(r) of the light wave 22_i to which the photodetector 16 is exposed in the spectral band λ_i.

The updating formula is:

$A_{i,p}^d\left(r\right)=m_i^d\left(r\right)\times \frac{F_{i,p}^d\left(r\right)}{F_{i,p}^d\left(r\right)}=m_i^d\left(r\right)\times e^{j{\tilde{\varphi }}_{i,p}^d\left(r\right)}$

where:

• • |F_i,p^d(r)| is the modulus of F_i,p^d(r);
  • m_i^d(r) is the modulus of the normalized initial complex amplitude A_i,p^d(r) determined, on the basis of the image I_i, in the first step 100. This term serves as a link to the measured data;
  • {tilde over (φ)}_i,p^d is an estimation of the phase of the complex amplitude of the wave 22_i in the i^th spectral band λ_i; and
  • A_i,p^d(r) is the complex amplitude of the light wave 22_i transmitted by the sample, in the plane of the photodetector 16, this complex amplitude forming the base of the following iteration.

Following this step, a new iteration may start, the input datum of this new iteration p+1 being A_i,p+1^d(r)=A_i,p^d(r), this new iteration starting with the back propagation of each normalized complex amplitude A_i,p+1^d(r), for the various spectral bands in question, to the sample plane P₀, according to step 200.

Steps 200 to 500 are carried out iteratively, either to a preset number of iterations p_max or until a convergence criterion is reached, the latter possibly being, for example, expressed in the form of a discrepancy between the estimation of two given quantities in two successive iterations. When this discrepancy is smaller than a given threshold ε, the convergence criterion is reached. For example, the process is stopped when one of these conditions is reached:

$\frac{F_{i,p}^d\left(r\right)}{F_{i,p}^d\left(r\right)}-\frac{F_{i,p+1}^d\left(r\right)}{F_{i,p+1}^d\left(r\right)}<\varepsilon ;$ $F_{i,p}^0\left(r\right)-F_{i,p+1}^0\left(r\right)<\varepsilon ;$ $A_{i,p}^0\left(r\right)-A_{i,p+1}^0\left(r\right)<\varepsilon ;$ $\mathrm{Arg}\left(A_{i,p}^0\left(r\right)-A_{i,p+1}^0\left(r\right)\right)<\varepsilon ;$

this list is not limiting.

At the end of the method, an estimation of the complex amplitude of the light wave 22_i, transmitted by the sample, and to which the photodetector is exposed, in the detector plane P, of equation z=d, and/or in the sample plane P₀, of equation z=0, is obtained, for each spectral band in question. Using the various complex amplitudes A_i,p⁰(r) reconstructed in the sample plane, a precise representation of the latter is obtained, in each of the spectral bands in question, in particular by forming images on the basis of the modulus or of the phase of said complex amplitudes.

As previously mentioned, when the spectral bands are spread over the visible spectrum, the modulus or phase images may be combined, for example superposed, so as to obtain representations in color.

It will be recalled that this algorithm, although described in relation to a normalized complex amplitude A_i, also applies to the non-normalized complex amplitude α_i.

Contribution of the Weighting Function

One of the important points of this iterative algorithm is the construction of the weighting function F⁰(r) in the sample plane. Specifically, generally, it is insufficient to determine the complex amplitude of a light wave on the basis of an image acquired by a photodetector, because information as to the phase of the wave is not recorded by the photodetector, the latter being sensitive only to intensity, which corresponds to the modulus of the complex amplitude of the wave.

Thus, as indicated in the description of step 100, the complex amplitude α_i^d(r) or normalized complex amplitude A_i^d(r) determined in this step contains no information as to the phase of the light wave that they represent. This lack of information results, during the back propagation from the detector plane P to the sample plane P₀, which is the subject matter of step 200, in the formation of artefacts that are referred to as twin images.

The inventors have observed that these artefacts mainly affect poor zones 31 located in the vicinity of diffracting elements 32, i.e. zones located between two adjacent diffracting elements 32. Furthermore, they have observed that these artefacts are liable to fluctuate as a function of wavelength. Thus, artefacts in the poor zones 31 may be averaged out statistically by combining, for various wavelengths, the complex amplitudes back propagated to the sample plane. This statistical smoothing then increases the signal-to-noise ratio in the complex image back propagated to the sample plane. Generally, the method amounts to:

• • obtaining an initial estimation A_i,p=1^d(r) of the complex amplitude of the wave 22_i transmitted by the sample, in the detector plane, and in a plurality of spectral bands (step 100);
  • back propagating each of these complex amplitudes to the sample plane, in order to obtain, in each spectral band, a complex amplitude A_i,p⁰(r) in the sample plane (step 200);
  • calculating a weighting function F_p⁰(r) weighting each complex amplitude in the sample plane (step 300), so as to decrease the influence of twin-image interfacts;
  • propagating said weighting function to the detector plane, for at least one spectral band (step 400);
  • updating the estimation of the complex amplitude A_i,p^d(r) of the wave 22_i transmitted by the sample, in the detector plane, and in a plurality of spectral bands, using the weighting function F_i,p^d(r) propagated to the detector plane (step 500).

The updating formula of step 500 shows that in each iteration, the modulus m_i^d(r) (M_i^d(r), respectively) of the normalized complex amplitude A_i,p^d(r) (of the complex amplitude α_i^d(r), respectively), in the detection plane, corresponds to that determined, in step 100, with each image I_i formed by the photodetector 16 in the spectral band λ_i. In other words, in the various iterations, the modulus, in the detection plane, of the complex amplitude α_i^d(r) or of the normalized complex amplitude A_i,p^d(r) does not vary and corresponds to that derived from the intensity measured by the photodetector.

In contrast, the algorithm tends to cause, in each update, a variation in the argument of the complex expression A_i,p^d(r) or α_i^d(r), and in particular in the estimation of the phase {tilde over (φ)}_i,p^d, the latter being considered to be equal to the phase of the weighting function F_i,p^d(r) propagated to the detector plane, at each wavelength λ_i.

Thus, in this algorithm, each iteration comprises:

• • updating the complex amplitude A_i,p^d(r) of each light wave in the sample plane P₀ (step 200);
  • updating the argument of each complex amplitude A_i,p^d(r), and in particular its phase, in the detection plane (step 500).

Generation of the Weighting Function

A first way of calculating the weighting function consists in assigning an equal weight to the various spectral bands λ_i in question.

For example, the weighting function may take the form

$F_p^0\left(r\right)=\frac{1}{\sum _ik_i}\sum _ik_iA_{i,p}^0\left(r\right),$

where k_ii is the weighting factor, or weight, attributed to the i^th spectral band λ_i, as described above with reference to step 300. Each weighting factor k_i is positive and may have the same value, for example ⅓.

According to one variant, and this applies in particular in the case where the sample analyzed is dyed, in a spectral range λ₀, the moduli of the complex amplitudes of first light waves 22_i the spectral bands λ_i of which are close to the spectral range λ₀ have a higher value than the moduli of the complex amplitudes of second light waves the spectral bands of which are further from the wavelength λ₀. In such a case, it is preferable to under-weight the complex amplitudes of the first light waves, and to over-weight the complex amplitudes of the second light waves.

For example, if the sample is dyed using a blue dye, which corresponds in our example to the first spectral band λ₁, the weighting factor k₁ is lower than the weighting factors k₂ and k₃ associated with the spectral bands λ₂ (green) and λ₃ (red), respectively.

According to another variant, the modulus and the argument of each complex amplitude are weighted by independent weighting factors, such that

$F_p^0\left(r\right)=\frac{1}{\sum _ik_i}\sum _ik_iA_{i,p}^0\left(r\right)$ $\mathrm{Arg}\left(F_p^0\left(r\right)\right)=\frac{1}{\sum _ik_i^{\prime }}\sum _ik_i^{\prime }\mathrm{Arg}\left(A_{i,p}^0\left(r\right)\right)$

k_i and k′_i being weighting factors respectively associated with the modulus and the argument of the complex amplitude of the light wave 22_i, in the sample plane, in the spectral band λ_i.

According to another variant, the combination of the complex amplitudes A_i,p⁰(r) takes the form of a sum of logarithms, according to the expression:

$\ln \left(F_p^0\left(r\right)\right)=\frac{1}{\sum _ik_i}\sum _ik_i\ln \left[A_{i,p}^0\left(r\right)\right]$

According to another variant, rather than one weighting function F_p⁰(r), a plurality of weighting functions F_i,p⁰(r) are determined in the sample plane, each function being associated with one spectral band λ_i.

Each weighting function F_i,p⁰(r) associated with an i^th wavelength is obtained by combining a plurality of complex amplitudes A_i,p⁰(r), respectively associated with various spectral bands.

In a first example, considering three spectral bands:

$\left[\begin{array}{c}{F}_{1,p}^0\\ F_{2,p}^0\\ F_{3,p}^0\end{array}\right]=\left[\begin{array}{ccc}{k}_{1,1}& k_{1,2}& k_{1,3}\\ k_{2,1}& k_{2,2}& k_{2,3}\\ k_{3,1}& k_{3,2}& k_{3,3}\end{array}\right]\left[\begin{array}{c}{A}_{1,p}^0\left(r\right)\\ A_{2,p}^0\left(r\right)\\ A_{3,p}^0\left(r\right)\end{array}\right]$

Thus, according to this embodiment, the weighting function takes the form of a vector {right arrow over (F_p⁰)}(r), of dimension N, N being the number of spectral bands in question, each term F_i,p⁰(r) of which is a weighting function associated with one spectral band λ_i. This weighting function may be obtained via the following matrix product:

{right arrow over (F_p⁰)}(r)=K{right arrow over (A_p⁰)}

Where K is a weighting matrix, each term k_i,j of the weighting matrix representing the weight associated with the complex amplitude A_j,p⁰(r) associated with the spectral band λ_j for the calculation of the weighting function associated with the spectral band λ_i.

The matrix K is a square matrix of N by N size, N being the number of spectral bands in question.

The weighting function is preferably normalized, such that each term F_i,p⁰ may be expressed in the form:

$F_{i,p}^0\left(r\right)=\frac{1}{\sum _jk_{i,j}}\sum _jk_{i,j}A_j^0\left(r\right)$

the term

$\frac{1}{\sum _jk_{i,j}}$

being a normalization term.

According to a second example of this embodiment, again considering three spectral bands,

$\left[\begin{array}{c}F_{1,p}^0\left(r\right)\\ F_{2,p}^0\left(r\right)\\ F_{3,p}^0\left(r\right)\\ \arg \left(F_{1,p}^0\left(r\right)\right)\\ \arg \left(F_{2,p}^0\left(r\right)\right)\\ \arg \left(F_{3,p}^0\left(r\right)\right)\end{array}\right]=\left[\begin{array}{cccccc}{k}_{1,1}& k_{1,2}& k_{1,3}& k_{1,4}& k_{1,5}& k_{1,6}\\ k_{2,1}& k_{2,2}& k_{2,3}& k_{2,4}& k_{2,5}& k_{2,6}\\ k_{3,1}& k_{3,2}& k_{3,3}& k_{3,4}& k_{3,5}& k_{3,6}\\ k_{4,1}& k_{4,2}& k_{4,3}& k_{4,4}& k_{4,5}& k_{4,6}\\ k_{5,1}& k_{5,2}& k_{5,3}& k_{5,4}& k_{5,5}& k_{5,6}\\ k_{6,1}& k_{6,2}& k_{6,3}& k_{6,4}& k_{6,5}& k_{6,6}\end{array}\right]\left[\begin{array}{c}A_{1,p}^0\left(r\right)\\ A_{2,p}^0\left(r\right)\\ A_{3,p}^0\left(r\right)\\ \arg \left(A_{1,p}^0\left(r\right)\right)\\ \arg \left(A_{2,p}^0\left(r\right)\right)\\ \arg \left(A_{3,p}^0\left(r\right)\right)\end{array}\right].$

Thus, according to this embodiment, the weighting function takes the form of a vector {right arrow over (F_p⁰)}(r), of dimension 2N, N being the number of spectral bands in question, each term of which is either the modulus or the argument of a weighting function F_i,p⁰(r) associated with one spectral band λ_i. This weighting function may be obtained via the following matrix product:

{right arrow over (F_p⁰)}(r)=K{right arrow over (A_p⁰)}

Where K is a weighting matrix, of 2N×2N size, each term k_i,j of the weighting matrix representing the weight associated either with the argument or with the phase of the complex amplitude A_j,p⁰(r) associated with the spectral band λ_j.

According to this embodiment, each coordinate of the vector {right arrow over (A_p⁰)} represents either the modulus, or the argument, of a complex amplitude A_j,p⁰(r), in a spectral band j.

Just as in the preceding example, the weighting function is preferably normalized, such that each term F_i,p⁰ may be expressed in the form:

$F_{i,p}^0\left(r\right)=\frac{1}{\stackrel{3}{\sum _{j=1}}k_{i,j}}\underset{j=1}{\sum ^3}k_{i,j}A_{j,p}^0\left(r\right)$ $\mathrm{Arg}\left(F_{i,p}^0\left(r\right)\right)=\frac{1}{\stackrel{6}{\sum _{j=4}}k_{i,j}}\stackrel{6}{\sum _{j=4}}k_{i,j}\arg \left(A_{j,p}^0\left(r\right)\right)$

Whatever the circumstances, the coefficients of a weighting matrix may be determined beforehand, either arbitrarily or on the basis of experimental trials.

For example, it is possible to establish a linear regression coefficient ac between two components i and j of the vector {right arrow over (A_p⁰)}(r), by considering a plurality of axial positions (r) in the sample plane, so as to obtain a statistically significant sample. The coefficient k_ij of weighting matrix may then be determined depending on this linear regression coefficient α_ij, optionally assigned a term taking into account the dispersion around the linear regression model. In such a case, the diagonal of the weighting matrix may consist of coefficients k_ii equal to 1.

This allows a weighting function F_i,p⁰, associated with the wavelength λ_i, taking into account the correlation between the various terms of the vector {right arrow over (A_p⁰)}(r) to be established.

Variants Regarding the Light Source or the Photodetector.

In the examples given with reference to FIGS. 1 and 2, the light source 11, able to emit a light wave 12 in various spectral bands, includes three elementary light sources 11₁, 11₂, 11₃, taking the form of light-emitting diodes emitting in a first spectral band λ₁, a second spectral band λ₂, and a third spectral band λ₃, respectively, the spectral bands being different from one another, and, preferably, not overlapping.

The light source 11 may also include a white light source 11_w placed upstream of a filtering device 19, for example a filter wheel, able to place a filter of pass band λ_i between the white light source and the sample, as shown in FIG. 5, such that the image I_i formed by the photodetector 16 is representative of said pass band λ_i. A plurality of filters, having pass bands that are different from one another, are then successively placed between the light source 11_w and the sample 10.

According to one variant, the filtering device 19 may also be a tri-band filter, defining a plurality of spectral bands. An example of a filter suitable for this application is the Edmund Optics 458, 530 & 628 nm tri-band filter, which defines spectral bands centered on the wavelength of 457 nm, 530 nm and 628 nm, respectively. This allows the sample to be illuminated simultaneously using 3 wavelengths.

The use of a diffuser 17, such as described above, between the light source and the diaphragm 18 is preferable, whatever the embodiment.

The photodetector 16 may, as described above, be an RGB matrix-array photodetector, this allowing the various images I₁ . . . I_i . . . I_N to be acquired in the various spectral bands λ₁ . . . λ_i . . . λ_N in succession or simultaneously. In this case, the light source may be a white light source 11_w, in which case the various images may be acquired simultaneously.

It may also be a question of a monochromatic photodetector 16, in which case the light source 11 is able to generate, in succession, a light wave in various spectral bands λ₁ . . . λ_i . . . λ_N. In such a configuration, the light source includes either a plurality of elementary light sources 11₁, 11₂, 11₃, or a filtering device 19, as described above. In such a case, the sample is exposed in succession to incident light waves 12₁ . . . 12_i . . . 12_N, N being the number of spectral bands in question. An image I_i (1≤i≤N), representative of the light wave 22_i transmitted by the sample is then acquired on each exposure.

Realized Trials.

Trials were carried out in the configuration shown in FIG. 1 and described above. The sample was an anatomopathology slide, including a cross section of colon stained with hematoxylin eosin saffron. The light source was placed at a distance Δ equal to 5 cm from the sample, this distance separating the diaphragm 18 from the sample 10.

FIGS. 6A, 6B and 6C show an image of the modulus |A_1,p⁰(r)| of the complex amplitude A_1,p⁰(r) of the wave 22₁ transmitted by the sample, in the plane P₀ of the sample, in the first spectral band λ₁ extending between 450 and 465 nm, these images being obtained after a number of iterations p equal to 1, 3 and 10, respectively.

FIGS. 7A, 7B and 7C show an image of the modulus |A_2,p⁰(r)| of the complex amplitude A_2,p⁰(r) of the wave 222 transmitted by the sample, in the plane P₀ of the sample, in the third spectral band λ₂ extending between 520 and 535 nm, these images being obtained after a number of iterations p equal to 1, 3 and 10, respectively.

FIGS. 8A, 8B and 8C show an image of the modulus |A_3,p⁰(r)| of the complex amplitude A_3,p⁰(r) of the wave 223 transmitted by the sample, in the plane P₀ of the sample, in the third spectral band λ₃ extending between 620 and 630 nm, these images being obtained after a number of iterations p equal to 1, 3 and 10, respectively. It will be noted that the average grayscale level of these images is higher than the grayscale level of the images of FIGS. 6A, 6B, 6C, 7A, 7B and 7C. This is due to the fact that the sample had the red-violet color of the sample.

FIGS. 9A, 9B and 9C show the combination of the images 6A-7A-8A, 6B-7 B-8B, and 6C-7C-8C, respectively. These figures allow a color representation of the sample to be obtained, by simultaneously taking into account the three spectral bands λ₁, λ₂ and λ₃.

In each series of images, an increase in contrast as a function of the number of iterations may be seen. It may also be noted that images the spatial resolution of which is satisfactory are formed when the number of iterations is lower than or equal to 10, this limiting the calculation time to a few seconds.

The method is therefore suitable for the high-rate, large-field observation of samples. It allows images to be obtained in one or more spectral bands, making it compatible with the staining methods commonly used in the field(s) of anatomical pathology and/or cytopathology.

Claims

1.-10. (canceled)

11. A method for observing a sample, comprising:

i) illuminating the sample using a light source that produces a light wave that propagates along a propagation axis;

ii) acquiring, using a photodetector, a plurality of images of the sample, the images being formed in a detection plane, the sample being placed between the light source and the photodetector, each image of the plurality being representative of a light wave, transmitted by the sample under the effect of the illumination, called the transmitted light wave, and each image of the plurality being acquired in a spectral band that is different from that of other images of the plurality;

iii) determining, based on each image respectively acquired in each spectral band, an initial complex amplitude of the transmitted light wave, in said each spectral band, in the detection plane;

iv) selecting a sample plane, in which the sample lies, and back-propagating each complex amplitude established in the detection plane, in said each spectral band, in order to determine a complex amplitude of the transmitted wave, in said each spectral band, in the sample plane;

v) calculating in said each spectral band, based on a plurality of complex amplitudes determined in step iv), a weighting function in the sample plane, the weighting function being calculated based on a weighted sum of the complex amplitudes, or on logarithm functions thereof, or on argument functions thereof;

vi) propagating the weighting function to the detection plane so as to obtain, for at least one spectral band, a weighting function in the detection plane;

vii) updating at least one complex amplitude of the transmitted light wave, in a spectral band, in the detection plane, using the weighting function obtained, in the spectral band, in step vi); and

viii) repeating steps iv) to vii) until a stop criterion is reached.

12. The method of claim 11, wherein, in step iii), a modulus of the complex amplitude of the transmitted light wave in a spectral band is determined by normalizing the intensity of the image acquired by the photodetector, in the spectral band, by a reference intensity measured by the photodetector in the absence of sample.

13. The method of claim 11, wherein, in step iv), the complex amplitude in a sample plane, in a spectral band, is determined by applying a propagation operator to the complex amplitude, defined in the same spectral band, in the detection plane.

14. The method of claim 11, wherein, in step vi), the weighting function, in the detection plane, is propagated by applying a propagation operator to the weighting function determined, in the sample plane, in step v).

15. The method of claim 11, wherein, in step vii), a modulus of the complex amplitude of the transmitted light wave, in a spectral band, in the detection plane, is calculated depending on a modulus of the initial complex amplitude, in the spectral band.

16. The method of claim 11, wherein, in step vii), an argument function of the complex amplitude of the transmitted light wave, in a spectral band, in the detection plane, is calculated depending on an argument function of the weighting function determined, in the detection plane and in the spectral band, in step vi).

17. The method of claim 11, wherein, in step v), the weighting function is common to all the spectral bands.

18. The method of claim 11, wherein step v) further comprises determining a plurality of weighting functions, each weighting function of said plurality of weighting functions being associated with one spectral band.

19. The method of claim 11, further comprising, following step viii):

ix) forming an image representative of a modulus or of an argument function of the complex amplitude of the wave transmitted by the sample, in the sample plane or in the detection plane, in at least one spectral band.

20. A device for observing a sample, comprising:

a light source configured to illuminate the sample;

a photodetector,

the sample being disposed between the light source and the photodetector,

the photodetector being configured to form a plurality of images, in a detection plane, of the light wave transmitted by the sample under the effect of illumination by the light source, each image being obtained in a spectral band that is different from that of other images of the plurality; and

a processor, configured to process the plurality of images by executing instructions, programmed into a memory, which, when executed, implement the method of claim 11.